{"id":"W4223558080","doi":"10.1093/humrep/deac072","title":"#ESHREjc report: seeing is believing! How time lapse imaging can improve IVF practice and take it to the future clinic","year":2022,"lang":"en","type":"article","venue":"Human Reproduction","topic":"Reproductive Biology and Fertility","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial insemination; Assisted reproductive technology; Embryo transfer; Human fertilization; Gynecology; Embryo; Artificial intelligence; Biology; Medicine; Computer science; Infertility; Pregnancy; Anatomy; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003831619,0.000195328,0.0002892292,0.00009087166,0.001120993,0.00004595014,0.0001217964,0.00006136933,0.000368203],"category_scores_gemma":[0.001774396,0.0001537231,0.00008879,0.0002785036,0.0001420032,0.0001634378,0.0002472216,0.0007652716,0.00002991154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001553577,"about_ca_system_score_gemma":0.00008864123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008216554,"about_ca_topic_score_gemma":0.00001769568,"domain_scores_codex":[0.9972147,0.0003258603,0.0003814612,0.00148618,0.0003128431,0.0002789689],"domain_scores_gemma":[0.9974458,0.00005454555,0.0003596288,0.001744338,0.0002987329,0.00009691443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.006756774,0.001135803,0.07740759,0.0003060159,0.0007552685,0.00100732,0.02579083,0.00001957595,0.3127702,0.0001475991,0.421654,0.1522491],"study_design_scores_gemma":[0.0004189468,0.0004216975,0.1482603,0.00001624542,0.0002370929,0.003642251,0.004770103,0.00001693592,0.002964041,0.000187362,0.8388466,0.0002183677],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6935709,0.00245385,0.00001738949,0.2991311,0.001773449,0.001120014,0.00001366197,0.0001241231,0.001795538],"genre_scores_gemma":[0.9508889,0.00005316818,0.0003088416,0.004611032,0.005053165,0.0001362643,0.00008270716,0.0000363458,0.0388296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4171927,"threshold_uncertainty_score":0.8621883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01974541786061206,"score_gpt":0.3091059387603278,"score_spread":0.2893605208997158,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}